ai-agents-dashboard

name	ai-agents-dashboard
description	Live real-time dashboard for monitoring AI agent swarms with smart completion detection, clickable agent detail views, idle time tracking, and activity streaming. Auto-launches a beautiful web UI with capybara-inspired colors.
version	6.0.0
author	HappyCapy
triggers	["launch dashboard","agent dashboard","monitor agents","live dashboard","show agent progress","track swarm","ai dashboard"]

AI Agents Dashboard Skill

Real-time monitoring for AI agent swarms with a beautiful, auto-refreshing web UI.

Features

• Real-time Updates: Auto-refreshes every 2 seconds
• Smart Completion Detection: Auto-detects when agents finish
• Last Activity Tracking: Shows "Xs/Xm/Xh ago" for each agent
• Idle State Detection: Agents idle >60s shown as "IDLE"
• Beautiful UI: Capybara-inspired color palette with light/dark themes
• Clickable Agent Details: Click any agent for live activity feed
• Zero Dependencies: Pure Python stdlib + vanilla HTML/CSS/JS
• Single File: Self-contained with embedded HTML

Quick Start

1. Create Swarm Configuration

Create swarm-config.json in your project directory:

{
  "swarm_name": "My Project Swarm",
  "start_time": "2026-02-05T14:00:00Z",
  "agents": {
    "agent-1": {
      "role": "Core Architect",
      "wave": 1,
      "task_id": "abc123",
      "mission": "Set up project structure"
    },
    "agent-2": {
      "role": "Backend Developer",
      "wave": 2,
      "task_id": "def456",
      "mission": "Build API endpoints"
    }
  }
}

2. Launch Dashboard

# Copy server to your workspace
cp ~/.claude/skills/ai-agents-dashboard/ai-agents-dashboard-server.py /workspace/my-project/

# Set environment variables
export SWARM_DIR="/workspace/my-project"
export SWARM_NAME="My Project Swarm"
export DASHBOARD_PORT=8080

# Start server
python3 /workspace/my-project/ai-agents-dashboard-server.py &

# Export port for web access
/app/export-port.sh 8080

3. View Dashboard

Open the exported URL in your browser. Dashboard auto-refreshes every 2 seconds.

Configuration

Environment Variables

Variable	Default	Description
SWARM_DIR	Current directory	Directory containing swarm-config.json
TASK_DIR	/tmp/claude-1000	Directory containing task output files
DASHBOARD_PORT	8080	HTTP server port
SWARM_NAME	From config	Display name for the swarm

API Endpoints

GET /

Returns the dashboard HTML page.

GET /api/status

Returns JSON with full swarm status.

GET /api/agent/{agent-id}

Returns detailed information about a specific agent.

GET /health

Returns {"status": "ok", "version": "v6"} for health checks.

Status States

Status	Color	Condition
pending	Gray	No output file exists
running	Orange (pulse)	Active output, <60s since last event
idle	Teal	60-120s since last event
completed	Green	>120s idle OR completion marker found
failed	Red	status.json has "status": "failed"

Files in This Skill

~/.claude/skills/ai-agents-dashboard/
├── SKILL.md                         # This file
├── ai-agents-dashboard-server.py    # Main server (self-contained)
└── launch-dashboard.py              # Launcher utility script

Using the Launcher Utility

from launch_dashboard import launch_dashboard, update_agent_task_id

# Launch dashboard
url = launch_dashboard(
    swarm_name="my-project",
    swarm_dir="/workspace/my-project",
    agents={
        "agent-1-core": {"role": "Core", "wave": 1, "mission": "Setup"},
        "agent-2-api": {"role": "API", "wave": 2, "mission": "Build API"},
    }
)

# Update agent task IDs after launching
update_agent_task_id(
    swarm_dir="/workspace/my-project",
    agent_id="agent-1-core",
    task_id="abc123def456"
)

Troubleshooting

Port already in use?

lsof -i :8080
export DASHBOARD_PORT=8081

Live Activity Feed Not Showing?

Call update_agent_task_id() after launching each agent to create symlinks.

Credits

Built for monitoring AI agent swarms in real-time.